Note that our tree-based convolution slightly differs from [1]} mou2016convolutional in that we have a deeper window but do not consider sibling information.
This is because our grammar-based generation obtains all siblings at a time by applying a certain rule, and hence, the siblings are less important than ancestors. The complexity of [1]} mou2016convolutional unfortunately grows exponentially with depth, and is less tractable, whereas our tree-based CNN variant grows linearly. In terms of the convolution computation, we follow [1]} mou2016convolutional and adopt a perceptron-like interaction. Deep tree-based convolution and shortcut connections as in ResNet could be explored as future work.
